Modelling sea-level fingerprints of glaciated regions with low mantleviscosity
Bibliographic record
Abstract
Abstract. Sea-level fingerprints define the spatially varying relative sea-level response to changes in grounded ice distribution. These fingerprints are a key component in generating regional sea-level projections. Calculation of these fingerprints is commonly based on the assumption that the isostatic response of the Earth is dominantly elastic on century time scales. While this assumption is accurate for regions underlain my mantle material with viscosity close to that of global average estimates, recent work focusing on the Antarctic region has shown that this assumption can led to significant error when the viscosity departs significantly from typical average values. Here we test this assumption for fingerprints associated with glaciers and ice caps. We compare output from a (1D) elastic Earth model to that of a 3D viscoelastic model which includes low viscosity mantle in three glaciated regions: Alaska, southwestern Canada and the southern Andes (Randolph Glacier Inventory (RGI) regions 1, 2 & 17, respectively). This comparison indicates that the error incurred by ignoring the non-elastic response is generally less than 1 cm over the 21st century but can reach magnitudes of up to several 10s of centimetres in low viscosity areas. This error can have large spatial gradients where crustal uplift in ice covered (or previously ice covered) areas changes into subsidence when moving away from the loading centres to areas peripheral to the mass loss. The existence of these large gradients indicates the need for loading models with high spatial resolution to accurately simulate sea-level fingerprints in these regions. We conclude that sea-level projections for Alaska, southwestern Canada and the southern Andes should not be based on elastic Earth models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".